CN112762885B - Bridge real-time deflection check coefficient calculation method based on monitoring data - Google Patents
Bridge real-time deflection check coefficient calculation method based on monitoring data Download PDFInfo
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Abstract
The invention discloses a bridge real-time deflection check coefficient calculation method based on monitoring data, which comprises the following steps: distributing traffic load and vertical deflection monitoring sensors at monitoring points; establishing a bridge finite element model, and acquiring a displacement influence line of a vertical deflection monitoring point; collecting deflection data and traffic load data in a specified monitoring period; acquiring actual displacement data under the action of vehicle load; obtaining theoretical displacement data under the action of vehicle-mounted load; respectively taking the maximum values of the actual displacement data and the theoretical displacement data in each set time period, and carrying out probability density distribution analysis; respectively judging whether the displacement probability distribution function conforms to the extreme value type I probability density distribution characteristic or not; and if the displacement probability distribution function accords with the I-type probability density distribution characteristic of the extreme value, calculating a check coefficient to evaluate the structural performance. The invention realizes the correlation analysis of vehicle-mounted action and displacement response, and calculates the bridge deflection check coefficient in real time in the data acquisition process so as to evaluate and monitor the structural performance of the bridge in real time.
Description
Technical Field
The invention belongs to the technical field of bridge monitoring, and particularly relates to a bridge real-time deflection check coefficient calculation method based on monitoring data.
Background
At present, one of the common methods for evaluating the bearing capacity of a bridge is to perform a load test on the bridge to be detected, a check coefficient is one of the most important indexes for evaluating the load test of the bridge, the check coefficient is mainly determined by the ratio of an actual measured value of a control section of a static load test of the bridge to a theoretical calculated value, the accuracy of a constant value range of the check coefficient directly determines the reliability of evaluating the bearing capacity of the bridge, the traditional check coefficient value range is mainly determined by the load test, but errors exist due to the accuracy of a test instrument, the selection of a test method, a field test environment, the experience of a tester and the like, and long-term and real-time evaluation on the structural performance of the bridge cannot be performed, so that a real-time and long-term calculation method for the check coefficient of the load test is required to truly reflect the actual safety reserve of the structure.
Disclosure of Invention
The invention aims to provide a bridge real-time deflection check coefficient calculation method based on monitoring data, and aims to solve the problems that the traditional check coefficient is rough in value and cannot evaluate the performance of a bridge structure for a long time in real time.
The technical scheme for realizing the purpose of the invention is as follows: a bridge real-time deflection check coefficient calculation method based on monitoring data comprises the following steps:
s1, setting monitoring points, and arranging traffic load and vertical deflection monitoring sensors at the monitoring points;
s2, establishing a bridge finite element model, and acquiring a displacement influence line of the vertical deflection monitoring point;
s3, collecting deflection data and traffic load data in a specified monitoring period;
s4, performing band-pass filtering on the deflection data to obtain actual displacement data under the action of vehicle load;
s5, loading the traffic load data on the displacement influence line of the deflection monitoring point to obtain theoretical displacement data under the action of vehicle load;
s6, respectively taking the maximum values of the actual displacement data and the theoretical displacement data in each set time period, carrying out probability density distribution analysis, fitting by using a least square method to obtain a theoretical displacement extreme value I type probability density distribution function and an actual displacement extreme value I type probability density distribution function, and obtaining the theoretical displacement probability distribution function and the actual displacement probability distribution function through integration;
s7, respectively judging whether the theoretical displacement probability distribution function and the actual displacement probability distribution function accord with the extreme value type I probability density distribution characteristic by using a K-S detection method;
if the theoretical and actual displacement probability distribution functions both accord with the extreme value I type probability density distribution characteristics, calculating a check coefficient to evaluate the structural performance;
and if the theoretical or actual displacement probability distribution function does not accord with the extreme value I type probability density distribution characteristic, returning to the step 3 and re-collecting the data.
Preferably, the deflection data acquisition frequency is above 10 Hz.
Preferably, the traffic load information includes: collecting time, lanes, axle number, vehicle speed, total weight, axle weight and axle distance.
Preferably, the specified monitoring period is 7 days.
Preferably, the extreme value type i probability density distribution function is specifically:
f(x)＝αe -α(x-μ) exp[e -α(x-μ) ],-∞<x<∞
in the formula (I), the compound is shown in the specification,
μ m-0.57722/α, σ denotes standard deviation, m denotes mean, and x denotes sample data.
Preferably, the displacement probability distribution function is specifically:
F(x)＝exp[e -α(x-μ) ],
wherein the content of the first and second substances,
μ m-0.57722/α, σ denotes standard deviation, m denotes mean, and x denotes sample data.
Preferably, the set time period is 10 min.
Preferably, the theoretical displacement probability distribution function F is respectively judged by using a K-S test method Theory of things (x) And the actual displacement probability distribution function F Fruit of Chinese wolfberry (x) The concrete steps of whether the probability density distribution characteristics of the extreme value I type are met or not are as follows:
s701, calculating the maximum absolute value D of the difference value between the actually measured probability distribution and the theoretical probability distribution by sample data n The calculation formula is as follows:
D n ＝max{|F n (x i )-F 0 (x i )|,|F n (x i-1 )-F 0 (x i-1 )|}
wherein i is more than or equal to 1 and less than or equal to n, F n (x i ) Representing the measured probability distribution, F 0 (x i ) Representing a theoretical probability distribution;
s702, determining a corresponding critical level according to the sample capacity value n;
s703, calculating D if the sample n ≤D n (β), considered to meet the extremum type i probability density distribution characteristics; otherwise, the extreme value type I probability density distribution characteristics are not met.
Preferably, the corresponding critical level calculation formula is:
preferably, the calculation formula of the check coefficient is:
in the formula (I), the compound is shown in the specification,
is the average value of the measured maximum values,
is the theoretical maximum mean.
Compared with the prior art, the invention has the following remarkable advantages: the invention calculates to obtain the check coefficient by utilizing the traffic load and deflection information in the real-time monitoring range, can realize continuous data updating and self-checking, and judges the change of the bridge structure performance by comparing with the historical reference data, thereby greatly reducing the errors caused by the precision degree of a testing instrument, the selection of a testing method, the field testing environment, the experience of a tester and the like.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow diagram of the present invention.
Fig. 2 is a schematic view of a main bridge according to an embodiment of the present invention, in which fig. 2(a) is a perspective view of the main bridge, and fig. 2(b) is a typical cross-sectional view of a box girder of the main bridge.
Fig. 3 is a schematic view of arrangement of monitoring points in the embodiment of the invention.
FIG. 4 is a graph illustrating the displacement time course of the main bridge across the midpoint according to the embodiment of the present invention.
FIG. 5 is a cross-medial vertical deformation maximum effect probability density distribution diagram in an embodiment of the present invention.
FIG. 6 is a graph showing a main bridge amplitude crack distribution monitored by a main bridge in an embodiment of the present invention, wherein FIG. 6(a) is a graph showing an in-box crack distribution, and FIG. 6(b) is a graph showing an out-box crack distribution.
Detailed Description
As shown in fig. 1, a method for calculating a real-time deflection check coefficient of a bridge based on monitoring data includes the following steps:
s1, setting monitoring points on the bridge, and laying traffic load and vertical deflection monitoring sensors at the monitoring points, wherein the monitoring points are put into use;
specifically, the distributed traffic load sensors realize monitoring of bridge full-section coverage, and the distributed deflection sensors select representative positions of quartering points, span and the like.
S2, establishing a bridge finite element model, and acquiring a displacement influence line of the vertical deflection monitoring point;
s3, collecting deflection data and traffic load data in a specified monitoring period;
specifically, the flexibility data acquisition frequency is above 10 Hz.
The traffic load data includes: collecting time, lanes, axle number, vehicle speed, total weight, axle weight and axle distance.
In certain embodiments, the specified monitoring period is 7 days.
S4, performing band-pass filtering on the deflection data to obtain actual displacement data under the action of vehicle load;
specifically, the threshold value of the low-frequency data of the filtered long-term load effect such as temperature is 1-2 Hz.
S5, loading the traffic load data on the displacement influence line of the deflection monitoring point to obtain theoretical displacement data under the action of vehicle load;
s6, in the monitoring period, the acquired deflection data sample is large, and the maximum value and the minimum value of all data are directly analyzed and are easily influenced by accidental factors. Therefore, in order to extract effective analysis samples, extreme value samples are adopted for analysis, the maximum value of the data samples in each set time period T is taken for probability density distribution analysis, and the least square method is used for fitting to obtain a theoretical displacement extreme value type I probability density distribution function f Theory of things (x) And actual displacement extreme value type I probability density distribution function f Fruit of Chinese wolfberry (x) Obtaining a theoretical displacement probability distribution function F by integration Theory of things (x) And the actual displacement probability distribution function F Fruit of Chinese wolfberry (x)；
The extreme value type I probability density distribution function is specifically as follows: f (x) ═ α e -α(x-μ) exp[e -α(x-μ) ],-∞<x<∞
The displacement probability distribution function is specifically: f (x) exp [ e -α(x-μ) ],
Wherein, the first and the second end of the pipe are connected with each other,
μ -0.57722/α, σ represents standard deviation, m represents mean, x represents sample data;
in particular, the data samples include theoretical displacement data samples and actual displacement data samples.
In certain embodiments, the value of T is 10 min.
S7, respectively judging theoretical displacement probability distribution functions F by using K-S inspection method Theory of things (x) And actual displacement probability distributionFunction F Fruit of Chinese wolfberry (x) Whether the probability density distribution characteristics of an extreme value I type are met or not;
s701, calculating the maximum absolute value D of the difference value between the actually measured probability distribution and the theoretical probability distribution by sample data n The calculation formula is as follows:
D n ＝max{|F n (x i )-F 0 (x i )|,|F n (x i-1 )-F 0 (x i-1 )|}
wherein i is more than or equal to 1 and less than or equal to n, F n (x i ) Representing the measured probability distribution, F 0 (x i ) Representing a theoretical probability distribution;
s702, determining a corresponding critical level by using the sample capacity value n
S703, making a judgment
If the sample is calculated to be D n ≤D n (β), considered to meet the extremum type i probability density distribution characteristics; otherwise, the extreme value type I probability density distribution characteristics are not met.
If the theoretical and actual displacement probability distribution functions both conform to the extreme value type I probability density distribution characteristics, calculating a check coefficient (an actually measured maximum value mean value and a theoretical maximum value mean value) to evaluate the structural performance, specifically:
obtaining the mean value of the measured maximum values
And the mean of theoretical maximum
Calculating a check coefficient
And if the theoretical or actual displacement probability distribution function does not accord with the extreme value I type probability density distribution characteristic, repeating the steps 3-6 for correction.
The method selects traffic load and deflection information in a real-time monitoring range, calculates to obtain a check coefficient, and evaluates the structural performance of the bridge in real time.
Examples
In the embodiment, a certain three-span prestressed concrete variable-section continuous box girder bridge is taken as an engineering example, the span combination is (58+103+58) m, and the upper structure consists of two single-box single-chamber sections which are separated from each other in an up-down direction. The bottom width of the single box is 6.25m, the cantilever length at two sides is 2.75m, and the full width is 11.75 m. The design load is highway class i (04 standard). The elevation, typically the cross-section, is shown in figure 2.
Under general conditions, the industry considers that sufficient safety storage is provided during the design of the PC variable cross-section continuous box girder bridge, but in the actual operation process, the PC variable cross-section continuous box girder bridge has various diseases which influence the stress performance of the structure besides the diseases of conventional concrete damage and peeling and the like, and parts of the bridges have serious structural downwarping. The real-time monitoring data of the PC continuous beam bridge comprise two contents of main span midspan deflection and vehicle load by combining the practical characteristics of the structure of the PC continuous beam bridge. The overall arrangement of the safety monitoring items is shown in table 1 and fig. 3.
TABLE 1 monitoring sensor arrangement
The check coefficient is one of important indexes for evaluating the working state and the structural performance of the bridge, the calculation of the check coefficient is mainly calculated according to an actual measured value and a theoretical value, and the expression of the check coefficient is as follows:
in the formula (I), the compound is shown in the specification,
is the average value of the measured maximum values,
is the theoretical maximum mean.
The smaller the check coefficient value is, the larger the actual rigidity safety reserve of the bridge structure is; the larger the check coefficient value is, the smaller the actual rigidity safety reserve of the bridge structure is; the closer the check coefficient value is to 1, the closer the actual working state of the structural member is to the calculated theoretical value.
The midspan deflection monitoring data in the three-span continuous beam 2020 from 4 to 6 months is used as an analysis sample, and the time course curve is shown in fig. 4. And performing frequency domain analysis on the original data, wherein the low-frequency data is from long-term load effects such as temperature and the like, and the high-frequency data is from vehicle loads. Based on the above, the original data is subjected to band-pass filtering, and an actual displacement time-course curve under the action of vehicle load and the like can be obtained. And (3) carrying out probability density distribution analysis on the midspan actual measurement displacement under the action of the vehicle load and the midspan theoretical displacement value of the actual vehicle loaded on the bridge structure according to the traffic load monitoring record at the stage, wherein the analysis result is shown in figure 5. As can be seen from the figure, the actual midspan deflection probability distribution characteristic is completely consistent with the theoretical midspan deflection probability distribution characteristic, the actually measured vertical displacement mean value is about 8.3mm and is smaller than the theoretical displacement mean value under the actual vehicle-mounted action by 10.4mm, the check coefficient is about 0.80, and compared with the early check coefficient of 0.7, the actual rigidity safety reserve of the bridge can be deduced to be reduced, and the bridge is analyzed to be in a high-load condition for a long time, so that the technical condition grade deterioration of the bridge in the operation period is aggravated, and the actual rigidity safety reserve is reduced.
To check the structural performance evaluation results, the bridge was manually inspected. The inspection shows that a large number of top and bottom plate longitudinal cracks exist inside and outside the bridge box, the web plate is along the non-structural oblique cracks of the steel bundle, in addition, a plurality of bottom plate transverse cracks and web plate vertical cracks exist in the midspan block section, the middle plate transverse cracks and the web plate vertical cracks partially penetrate through the bottom plate, the length of the cracks is 1.2-1.5 m, the width of the cracks is 0.08-0.15 mm, and the cracks are typical bending stress cracks. The distribution of cracks inside and outside the tank is shown in FIG. 6. According to the manual inspection result of the bridge, a large number of cracks exist inside and outside the main bridge box girder of the bridge, and a large number of structural cracks such as transverse cracks of a bottom plate, vertical cracks of a web plate and the like exist in the midspan block section, so that the rigidity of the bridge is obviously reduced due to the existence of the defects, and the defects are identical to the structural performance evaluation result based on the deflection check coefficient.
Claims (10)
1. A bridge real-time deflection check coefficient calculation method based on monitoring data is characterized by comprising the following steps:
s1, setting monitoring points, and arranging traffic load and vertical deflection monitoring sensors at the monitoring points;
s2, establishing a bridge finite element model, and acquiring a displacement influence line of the vertical deflection monitoring point;
s3, collecting deflection data and traffic load data in a specified monitoring period;
s4, performing band-pass filtering on the deflection data to obtain actual displacement data under the action of vehicle load;
s5, loading the traffic load data on the displacement influence line of the deflection monitoring point to obtain theoretical displacement data under the action of vehicle load;
s6, respectively taking the maximum values of the actual displacement data and the theoretical displacement data in each set time period, carrying out probability density distribution analysis, fitting by using a least square method to obtain a theoretical displacement extreme value I type probability density distribution function and an actual displacement extreme value I type probability density distribution function, and obtaining the theoretical displacement probability distribution function and the actual displacement probability distribution function through integration;
s7, respectively judging whether the theoretical displacement probability distribution function and the actual displacement probability distribution function accord with the extreme value type I probability density distribution characteristic by using a K-S detection method;
if the theoretical and actual displacement probability distribution functions both accord with the extreme value I type probability density distribution characteristics, calculating a check coefficient to evaluate the structural performance;
and if the theoretical or actual displacement probability distribution function does not accord with the extreme value I type probability density distribution characteristic, returning to the step 3 and re-collecting the data.
2. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the deflection data acquisition frequency is above 10 Hz.
3. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the traffic load information comprises: collecting time, lanes, axle number, vehicle speed, total weight, axle weight and axle distance.
4. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the specified monitoring period is 7 days.
5. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the extreme value type i probability density distribution function is specifically:
f(x)＝αe -α(x-μ) exp[e -α(x-μ) ],-∞<x<∞
in the formula (I), the compound is shown in the specification,
μ ═ m-0.57722/α, σ denotes standard deviation, m denotes mean, and x denotes sample data.
6. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the displacement probability distribution function is specifically as follows:
F(x)＝exp[e -α(x-μ) ],
wherein the content of the first and second substances,
μ m-0.57722/α, σ denotes standard deviation, m denotes mean, and x denotes sample data.
7. The bridge real-time deflection check coefficient calculation method based on the monitored data as recited in claim 1, wherein the set time period is 10 min.
8. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 1, wherein the theoretical displacement probability distribution function F is respectively judged by using a K-S inspection method Theory of things (x) And the actual displacement probability distribution function F Fruit of Chinese wolfberry (x) The concrete steps of whether the probability density distribution characteristics of the extreme value I type are met or not are as follows:
s701, calculating the maximum absolute value D of the difference value between the actually measured probability distribution and the theoretical probability distribution by sample data n The calculation formula is as follows:
D n ＝max{|F n (x i )-F 0 (x i )|,|F n (x i-1 )-F 0 (x i-1 )|}
wherein i is more than or equal to 1 and less than or equal to n, F n (x i ) Representing the measured probability distribution, F 0 (x i ) Representing a theoretical probability distribution;
s702, determining a corresponding critical level according to a sample capacity value n;
s703, calculating D if the sample n ≤D n (β), considered to meet the extremum type i probability density distribution characteristics; otherwise, the extreme value type I probability density distribution characteristics are not met.
9. The method for calculating the real-time deflection check coefficient of the bridge based on the monitored data as claimed in claim 8, wherein the corresponding critical level calculation formula is as follows:
10. the bridge real-time deflection check coefficient calculation method based on the monitored data as claimed in claim 1, wherein the check coefficient has a calculation formula as follows:
in the formula (I), the compound is shown in the specification,
is the mean value of the measured maximum values,
is the theoretical maximum mean.
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